Overview

Dataset statistics

Number of variables23
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory225.0 KiB
Average record size in memory192.0 B

Variable types

Categorical14
Numeric9

Alerts

IDENPA has constant value "604"Constant
P13ST.D is highly overall correlated with P13ST.EHigh correlation
P13ST.E is highly overall correlated with P13ST.DHigh correlation
P9STGBS is highly imbalanced (52.6%)Imbalance
S6 is highly imbalanced (58.2%)Imbalance
SEXO is uniformly distributedUniform

Reproduction

Analysis started2025-07-30 18:27:44.446764
Analysis finished2025-07-30 18:27:59.293317
Duration14.85 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

IDENPA
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
604
1200 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3600
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row604
2nd row604
3rd row604
4th row604
5th row604

Common Values

ValueCountFrequency (%)
604 1200
100.0%

Length

2025-07-30T20:27:59.418429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:27:59.569084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
604 1200
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1200
33.3%
0 1200
33.3%
4 1200
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1200
33.3%
0 1200
33.3%
4 1200
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1200
33.3%
0 1200
33.3%
4 1200
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1200
33.3%
0 1200
33.3%
4 1200
33.3%

SEXO
Categorical

UNIFORM 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2
600 
1
600 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 600
50.0%
1 600
50.0%

Length

2025-07-30T20:27:59.719937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:27:59.878695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 600
50.0%
1 600
50.0%

Most occurring characters

ValueCountFrequency (%)
2 600
50.0%
1 600
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 600
50.0%
1 600
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 600
50.0%
1 600
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 600
50.0%
1 600
50.0%

P9STGBS
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2
1014 
1
137 
-5
 
49

Length

Max length2
Median length1
Mean length1.0408333
Min length1

Characters and Unicode

Total characters1249
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 1014
84.5%
1 137
 
11.4%
-5 49
 
4.1%

Length

2025-07-30T20:28:00.036728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:00.180226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 1014
84.5%
1 137
 
11.4%
5 49
 
4.1%

Most occurring characters

ValueCountFrequency (%)
2 1014
81.2%
1 137
 
11.0%
- 49
 
3.9%
5 49
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1014
81.2%
1 137
 
11.0%
- 49
 
3.9%
5 49
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1014
81.2%
1 137
 
11.0%
- 49
 
3.9%
5 49
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1014
81.2%
1 137
 
11.0%
- 49
 
3.9%
5 49
 
3.9%

P13STGBS.B
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8766667
Minimum-2
Maximum4
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.3%
Memory size18.8 KiB
2025-07-30T20:28:00.318894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.96206039
Coefficient of variation (CV)0.33443582
Kurtosis0.48808268
Mean2.8766667
Median Absolute Deviation (MAD)1
Skewness-0.65781615
Sum3452
Variance0.92556019
MonotonicityNot monotonic
2025-07-30T20:28:00.470787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 453
37.8%
4 357
29.8%
2 285
23.8%
1 101
 
8.4%
-2 2
 
0.2%
-1 2
 
0.2%
ValueCountFrequency (%)
-2 2
 
0.2%
-1 2
 
0.2%
1 101
 
8.4%
2 285
23.8%
3 453
37.8%
4 357
29.8%
ValueCountFrequency (%)
4 357
29.8%
3 453
37.8%
2 285
23.8%
1 101
 
8.4%
-1 2
 
0.2%
-2 2
 
0.2%

P13ST.D
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6191667
Minimum-2
Maximum4
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.5%
Memory size18.8 KiB
2025-07-30T20:28:00.615028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile2
Q13
median4
Q34
95-th percentile4
Maximum4
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70442375
Coefficient of variation (CV)0.194637
Kurtosis13.368289
Mean3.6191667
Median Absolute Deviation (MAD)0
Skewness-2.8799345
Sum4343
Variance0.49621282
MonotonicityNot monotonic
2025-07-30T20:28:00.765919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 842
70.2%
3 292
 
24.3%
2 47
 
3.9%
1 13
 
1.1%
-1 4
 
0.3%
-2 2
 
0.2%
ValueCountFrequency (%)
-2 2
 
0.2%
-1 4
 
0.3%
1 13
 
1.1%
2 47
 
3.9%
3 292
 
24.3%
4 842
70.2%
ValueCountFrequency (%)
4 842
70.2%
3 292
 
24.3%
2 47
 
3.9%
1 13
 
1.1%
-1 4
 
0.3%
-2 2
 
0.2%

P13ST.E
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4883333
Minimum-2
Maximum4
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)0.6%
Memory size18.8 KiB
2025-07-30T20:28:00.908189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile2
Q13
median4
Q34
95-th percentile4
Maximum4
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79291939
Coefficient of variation (CV)0.22730608
Kurtosis7.7231179
Mean3.4883333
Median Absolute Deviation (MAD)0
Skewness-2.1887651
Sum4186
Variance0.62872116
MonotonicityNot monotonic
2025-07-30T20:28:01.066591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 745
62.1%
3 337
28.1%
2 93
 
7.8%
1 18
 
1.5%
-1 5
 
0.4%
-2 2
 
0.2%
ValueCountFrequency (%)
-2 2
 
0.2%
-1 5
 
0.4%
1 18
 
1.5%
2 93
 
7.8%
3 337
28.1%
4 745
62.1%
ValueCountFrequency (%)
4 745
62.1%
3 337
28.1%
2 93
 
7.8%
1 18
 
1.5%
-1 5
 
0.4%
-2 2
 
0.2%

P13ST.F
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
4
603 
3
378 
2
166 
1
 
46
-1
 
7

Length

Max length2
Median length1
Mean length1.0058333
Min length1

Characters and Unicode

Total characters1207
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 603
50.2%
3 378
31.5%
2 166
 
13.8%
1 46
 
3.8%
-1 7
 
0.6%

Length

2025-07-30T20:28:01.249869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:01.412462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 603
50.2%
3 378
31.5%
2 166
 
13.8%
1 53
 
4.4%

Most occurring characters

ValueCountFrequency (%)
4 603
50.0%
3 378
31.3%
2 166
 
13.8%
1 53
 
4.4%
- 7
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 603
50.0%
3 378
31.3%
2 166
 
13.8%
1 53
 
4.4%
- 7
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 603
50.0%
3 378
31.3%
2 166
 
13.8%
1 53
 
4.4%
- 7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 603
50.0%
3 378
31.3%
2 166
 
13.8%
1 53
 
4.4%
- 7
 
0.6%

P13ST.H
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
3
440 
4
387 
2
305 
1
58 
-1
 
10

Length

Max length2
Median length1
Mean length1.0083333
Min length1

Characters and Unicode

Total characters1210
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row-1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 440
36.7%
4 387
32.2%
2 305
25.4%
1 58
 
4.8%
-1 10
 
0.8%

Length

2025-07-30T20:28:01.623049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:01.790790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 440
36.7%
4 387
32.2%
2 305
25.4%
1 68
 
5.7%

Most occurring characters

ValueCountFrequency (%)
3 440
36.4%
4 387
32.0%
2 305
25.2%
1 68
 
5.6%
- 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 440
36.4%
4 387
32.0%
2 305
25.2%
1 68
 
5.6%
- 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 440
36.4%
4 387
32.0%
2 305
25.2%
1 68
 
5.6%
- 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 440
36.4%
4 387
32.0%
2 305
25.2%
1 68
 
5.6%
- 10
 
0.8%

P40STGBS
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
3
388 
4
378 
2
322 
1
108 
-1
 
4

Length

Max length2
Median length1
Mean length1.0033333
Min length1

Characters and Unicode

Total characters1204
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 388
32.3%
4 378
31.5%
2 322
26.8%
1 108
 
9.0%
-1 4
 
0.3%

Length

2025-07-30T20:28:01.973813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:02.350323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 388
32.3%
4 378
31.5%
2 322
26.8%
1 112
 
9.3%

Most occurring characters

ValueCountFrequency (%)
3 388
32.2%
4 378
31.4%
2 322
26.7%
1 112
 
9.3%
- 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 388
32.2%
4 378
31.4%
2 322
26.7%
1 112
 
9.3%
- 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 388
32.2%
4 378
31.4%
2 322
26.7%
1 112
 
9.3%
- 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 388
32.2%
4 378
31.4%
2 322
26.7%
1 112
 
9.3%
- 4
 
0.3%

P41ST.A
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
3
468 
2
346 
4
257 
1
96 
-5
 
33

Length

Max length2
Median length1
Mean length1.0275
Min length1

Characters and Unicode

Total characters1233
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 468
39.0%
2 346
28.8%
4 257
21.4%
1 96
 
8.0%
-5 33
 
2.8%

Length

2025-07-30T20:28:02.540422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:02.698725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 468
39.0%
2 346
28.8%
4 257
21.4%
1 96
 
8.0%
5 33
 
2.8%

Most occurring characters

ValueCountFrequency (%)
3 468
38.0%
2 346
28.1%
4 257
20.8%
1 96
 
7.8%
- 33
 
2.7%
5 33
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 468
38.0%
2 346
28.1%
4 257
20.8%
1 96
 
7.8%
- 33
 
2.7%
5 33
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 468
38.0%
2 346
28.1%
4 257
20.8%
1 96
 
7.8%
- 33
 
2.7%
5 33
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 468
38.0%
2 346
28.1%
4 257
20.8%
1 96
 
7.8%
- 33
 
2.7%
5 33
 
2.7%

P41ST.H
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
3
419 
2
406 
4
194 
1
156 
-5
 
25

Length

Max length2
Median length1
Mean length1.0208333
Min length1

Characters and Unicode

Total characters1225
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 419
34.9%
2 406
33.8%
4 194
16.2%
1 156
 
13.0%
-5 25
 
2.1%

Length

2025-07-30T20:28:02.897525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:03.074322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 419
34.9%
2 406
33.8%
4 194
16.2%
1 156
 
13.0%
5 25
 
2.1%

Most occurring characters

ValueCountFrequency (%)
3 419
34.2%
2 406
33.1%
4 194
15.8%
1 156
 
12.7%
- 25
 
2.0%
5 25
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 419
34.2%
2 406
33.1%
4 194
15.8%
1 156
 
12.7%
- 25
 
2.0%
5 25
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 419
34.2%
2 406
33.1%
4 194
15.8%
1 156
 
12.7%
- 25
 
2.0%
5 25
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 419
34.2%
2 406
33.1%
4 194
15.8%
1 156
 
12.7%
- 25
 
2.0%
5 25
 
2.0%

P44ST.B
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8666667
Minimum-2
Maximum4
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)1.9%
Memory size18.8 KiB
2025-07-30T20:28:03.239750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0463275
Coefficient of variation (CV)0.36499796
Kurtosis3.0494112
Mean2.8666667
Median Absolute Deviation (MAD)1
Skewness-1.2588254
Sum3440
Variance1.0948012
MonotonicityNot monotonic
2025-07-30T20:28:03.386267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 442
36.8%
4 366
30.5%
2 310
25.8%
1 59
 
4.9%
-1 17
 
1.4%
-2 6
 
0.5%
ValueCountFrequency (%)
-2 6
 
0.5%
-1 17
 
1.4%
1 59
 
4.9%
2 310
25.8%
3 442
36.8%
4 366
30.5%
ValueCountFrequency (%)
4 366
30.5%
3 442
36.8%
2 310
25.8%
1 59
 
4.9%
-1 17
 
1.4%
-2 6
 
0.5%

P45ST.A
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
3
693 
2
402 
1
72 
-5
 
33

Length

Max length2
Median length1
Mean length1.0275
Min length1

Characters and Unicode

Total characters1233
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
3 693
57.8%
2 402
33.5%
1 72
 
6.0%
-5 33
 
2.8%

Length

2025-07-30T20:28:03.568149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:03.729672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 693
57.8%
2 402
33.5%
1 72
 
6.0%
5 33
 
2.8%

Most occurring characters

ValueCountFrequency (%)
3 693
56.2%
2 402
32.6%
1 72
 
5.8%
- 33
 
2.7%
5 33
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 693
56.2%
2 402
32.6%
1 72
 
5.8%
- 33
 
2.7%
5 33
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 693
56.2%
2 402
32.6%
1 72
 
5.8%
- 33
 
2.7%
5 33
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 693
56.2%
2 402
32.6%
1 72
 
5.8%
- 33
 
2.7%
5 33
 
2.7%

P45S.B
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
3
669 
2
419 
1
97 
-5
 
15

Length

Max length2
Median length1
Mean length1.0125
Min length1

Characters and Unicode

Total characters1215
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 669
55.8%
2 419
34.9%
1 97
 
8.1%
-5 15
 
1.2%

Length

2025-07-30T20:28:03.902897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:04.065881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 669
55.8%
2 419
34.9%
1 97
 
8.1%
5 15
 
1.2%

Most occurring characters

ValueCountFrequency (%)
3 669
55.1%
2 419
34.5%
1 97
 
8.0%
- 15
 
1.2%
5 15
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 669
55.1%
2 419
34.5%
1 97
 
8.0%
- 15
 
1.2%
5 15
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 669
55.1%
2 419
34.5%
1 97
 
8.0%
- 15
 
1.2%
5 15
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 669
55.1%
2 419
34.5%
1 97
 
8.0%
- 15
 
1.2%
5 15
 
1.2%

P58ST
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
4
752 
2
316 
1
84 
3
 
46
-1
 
2

Length

Max length2
Median length1
Mean length1.0016667
Min length1

Characters and Unicode

Total characters1202
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 752
62.7%
2 316
26.3%
1 84
 
7.0%
3 46
 
3.8%
-1 2
 
0.2%

Length

2025-07-30T20:28:04.250845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:04.420894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 752
62.7%
2 316
26.3%
1 86
 
7.2%
3 46
 
3.8%

Most occurring characters

ValueCountFrequency (%)
4 752
62.6%
2 316
26.3%
1 86
 
7.2%
3 46
 
3.8%
- 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 752
62.6%
2 316
26.3%
1 86
 
7.2%
3 46
 
3.8%
- 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 752
62.6%
2 316
26.3%
1 86
 
7.2%
3 46
 
3.8%
- 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 752
62.6%
2 316
26.3%
1 86
 
7.2%
3 46
 
3.8%
- 2
 
0.2%

S2
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.465
Minimum-2
Maximum5
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)2.7%
Memory size18.8 KiB
2025-07-30T20:28:04.569889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum5
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1944164
Coefficient of variation (CV)0.34470892
Kurtosis3.9096641
Mean3.465
Median Absolute Deviation (MAD)1
Skewness-1.4173022
Sum4158
Variance1.4266305
MonotonicityNot monotonic
2025-07-30T20:28:04.725555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 478
39.8%
4 373
31.1%
5 220
18.3%
2 70
 
5.8%
-1 29
 
2.4%
1 27
 
2.2%
-2 3
 
0.2%
ValueCountFrequency (%)
-2 3
 
0.2%
-1 29
 
2.4%
1 27
 
2.2%
2 70
 
5.8%
3 478
39.8%
4 373
31.1%
5 220
18.3%
ValueCountFrequency (%)
5 220
18.3%
4 373
31.1%
3 478
39.8%
2 70
 
5.8%
1 27
 
2.2%
-1 29
 
2.4%
-2 3
 
0.2%

S3
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0141667
Minimum-2
Maximum4
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)0.8%
Memory size18.8 KiB
2025-07-30T20:28:04.886660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum4
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9990651
Coefficient of variation (CV)0.49601908
Kurtosis0.15371487
Mean2.0141667
Median Absolute Deviation (MAD)1
Skewness0.092267035
Sum2417
Variance0.99813108
MonotonicityNot monotonic
2025-07-30T20:28:05.039901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 426
35.5%
2 380
31.7%
3 295
24.6%
4 90
 
7.5%
-2 5
 
0.4%
-1 4
 
0.3%
ValueCountFrequency (%)
-2 5
 
0.4%
-1 4
 
0.3%
1 426
35.5%
2 380
31.7%
3 295
24.6%
4 90
 
7.5%
ValueCountFrequency (%)
4 90
 
7.5%
3 295
24.6%
2 380
31.7%
1 426
35.5%
-1 4
 
0.3%
-2 5
 
0.4%

S5
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4758333
Minimum-2
Maximum4
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)0.8%
Memory size18.8 KiB
2025-07-30T20:28:05.180235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum4
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93370721
Coefficient of variation (CV)0.37712846
Kurtosis0.93844394
Mean2.4758333
Median Absolute Deviation (MAD)1
Skewness-0.40687434
Sum2971
Variance0.87180915
MonotonicityNot monotonic
2025-07-30T20:28:05.328906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 460
38.3%
3 425
35.4%
4 161
 
13.4%
1 144
 
12.0%
-1 8
 
0.7%
-2 2
 
0.2%
ValueCountFrequency (%)
-2 2
 
0.2%
-1 8
 
0.7%
1 144
 
12.0%
2 460
38.3%
3 425
35.4%
4 161
 
13.4%
ValueCountFrequency (%)
4 161
 
13.4%
3 425
35.4%
2 460
38.3%
1 144
 
12.0%
-1 8
 
0.7%
-2 2
 
0.2%

S6
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
4
959 
2
166 
1
 
56
3
 
10
-5
 
9

Length

Max length2
Median length1
Mean length1.0075
Min length1

Characters and Unicode

Total characters1209
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row-5
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
4 959
79.9%
2 166
 
13.8%
1 56
 
4.7%
3 10
 
0.8%
-5 9
 
0.8%

Length

2025-07-30T20:28:05.505019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:05.670279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 959
79.9%
2 166
 
13.8%
1 56
 
4.7%
3 10
 
0.8%
5 9
 
0.8%

Most occurring characters

ValueCountFrequency (%)
4 959
79.3%
2 166
 
13.7%
1 56
 
4.6%
3 10
 
0.8%
- 9
 
0.7%
5 9
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 959
79.3%
2 166
 
13.7%
1 56
 
4.6%
3 10
 
0.8%
- 9
 
0.7%
5 9
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 959
79.3%
2 166
 
13.7%
1 56
 
4.6%
3 10
 
0.8%
- 9
 
0.7%
5 9
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 959
79.3%
2 166
 
13.7%
1 56
 
4.6%
3 10
 
0.8%
- 9
 
0.7%
5 9
 
0.7%

S18.A
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2841667
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 KiB
2025-07-30T20:28:05.819255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.3157904
Coefficient of variation (CV)0.7051379
Kurtosis-1.6183402
Mean3.2841667
Median Absolute Deviation (MAD)2
Skewness0.31143697
Sum3941
Variance5.362885
MonotonicityNot monotonic
2025-07-30T20:28:05.962593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 512
42.7%
6 325
27.1%
3 114
 
9.5%
7 82
 
6.8%
4 77
 
6.4%
2 65
 
5.4%
5 25
 
2.1%
ValueCountFrequency (%)
1 512
42.7%
2 65
 
5.4%
3 114
 
9.5%
4 77
 
6.4%
5 25
 
2.1%
6 325
27.1%
7 82
 
6.8%
ValueCountFrequency (%)
7 82
 
6.8%
6 325
27.1%
5 25
 
2.1%
4 77
 
6.4%
3 114
 
9.5%
2 65
 
5.4%
1 512
42.7%

S20.J
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2
654 
1
545 
-2
 
1

Length

Max length2
Median length1
Mean length1.0008333
Min length1

Characters and Unicode

Total characters1201
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 654
54.5%
1 545
45.4%
-2 1
 
0.1%

Length

2025-07-30T20:28:06.141739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:06.316577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 655
54.6%
1 545
45.4%

Most occurring characters

ValueCountFrequency (%)
2 655
54.5%
1 545
45.4%
- 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 655
54.5%
1 545
45.4%
- 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 655
54.5%
1 545
45.4%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 655
54.5%
1 545
45.4%
- 1
 
0.1%

REEEDUC.1
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6625
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 KiB
2025-07-30T20:28:06.469040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9498107
Coefficient of variation (CV)0.41818997
Kurtosis-1.0095652
Mean4.6625
Median Absolute Deviation (MAD)2
Skewness-0.38110715
Sum5595
Variance3.8017619
MonotonicityNot monotonic
2025-07-30T20:28:06.617165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 348
29.0%
7 323
26.9%
3 172
14.3%
2 111
 
9.2%
1 98
 
8.2%
6 83
 
6.9%
4 65
 
5.4%
ValueCountFrequency (%)
1 98
 
8.2%
2 111
 
9.2%
3 172
14.3%
4 65
 
5.4%
5 348
29.0%
6 83
 
6.9%
7 323
26.9%
ValueCountFrequency (%)
7 323
26.9%
6 83
 
6.9%
5 348
29.0%
4 65
 
5.4%
3 172
14.3%
2 111
 
9.2%
1 98
 
8.2%

REEDAD
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2
423 
3
337 
1
284 
4
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
2 423
35.2%
3 337
28.1%
1 284
23.7%
4 156
 
13.0%

Length

2025-07-30T20:28:06.798646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-30T20:28:06.964516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 423
35.2%
3 337
28.1%
1 284
23.7%
4 156
 
13.0%

Most occurring characters

ValueCountFrequency (%)
2 423
35.2%
3 337
28.1%
1 284
23.7%
4 156
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 423
35.2%
3 337
28.1%
1 284
23.7%
4 156
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 423
35.2%
3 337
28.1%
1 284
23.7%
4 156
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 423
35.2%
3 337
28.1%
1 284
23.7%
4 156
 
13.0%

Interactions

2025-07-30T20:27:57.196888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:46.006754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:47.562960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.921227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:50.251305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.643489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.022972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:54.557170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:55.889818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:57.338571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:46.173976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:47.709642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.066675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:50.406767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.797369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.168496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:54.700413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.032870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:57.492070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:46.326124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:47.862961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.213770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:50.552456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.947501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.317803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:54.846035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.175061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:57.635658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:46.625122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.009417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.368318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:50.713556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:52.100320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.465317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:54.989259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.328108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:57.784221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:46.780970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.152646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.511387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:50.888959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:52.258153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.619001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:55.142368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.468300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:57.929128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:46.947701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.310494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.661706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.045984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:52.416231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.766873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:55.286364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.624994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:58.069541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:47.109581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.455502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.806438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.204834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:52.567647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:53.916720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:55.439055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.769723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:58.211949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:47.261837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.615196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:49.957589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.346838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:52.722560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:54.060714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:55.586865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:56.915918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:58.359006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:47.411741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:48.768369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:50.106151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:51.497669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:52.872968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:54.210132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:55.738654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-07-30T20:27:57.050681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-07-30T20:28:07.122441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
P13ST.DP13ST.EP13ST.FP13ST.HP13STGBS.BP40STGBSP41ST.AP41ST.HP44ST.BP45S.BP45ST.AP58STP9STGBSREEDADREEEDUC.1S18.AS2S20.JS3S5S6SEXO
P13ST.D1.0000.5820.3010.1590.3430.0740.1280.0870.0380.0780.0660.0000.1830.1210.020-0.0560.1140.1050.0330.1410.0880.095
P13ST.E0.5821.0000.3820.2610.4010.1990.1580.1460.1070.1170.1040.0000.1860.108-0.047-0.0680.0990.0630.0690.1360.0780.161
P13ST.F0.3010.3821.0000.2460.4600.1160.1360.1410.0530.0790.0930.0000.1250.137-0.050-0.0950.1000.0570.0820.1090.0410.085
P13ST.H0.1590.2610.2461.0000.2360.1230.1490.1420.1150.1320.1480.0000.1330.117-0.069-0.0610.0600.0610.0680.1430.0490.077
P13STGBS.B0.3430.4010.4600.2361.0000.0660.1330.1330.0130.0290.0610.0000.0730.104-0.132-0.0820.1240.1280.0890.1400.0000.040
P40STGBS0.0740.1990.1160.1230.0661.0000.1830.1740.2480.2190.1390.0690.1460.140-0.184-0.0120.0540.1190.0260.1190.0000.130
P41ST.A0.1280.1580.1360.1490.1330.1831.0000.2810.0770.1600.1410.0000.1360.118-0.057-0.0210.0930.1010.0850.1360.0450.118
P41ST.H0.0870.1460.1410.1420.1330.1740.2811.0000.0530.1790.1100.0740.1280.0720.032-0.0080.0950.095-0.0070.0530.0810.093
P44ST.B0.0380.1070.0530.1150.0130.2480.0770.0531.0000.2450.1790.1110.1110.086-0.0860.0260.0290.0790.0050.0740.0650.083
P45S.B0.0780.1170.0790.1320.0290.2190.1600.1790.2451.0000.3580.1150.0990.113-0.0890.0070.0350.0710.0410.0920.0000.114
P45ST.A0.0660.1040.0930.1480.0610.1390.1410.1100.1790.3581.0000.0790.0000.106-0.060-0.0240.0330.1220.0570.1580.0530.131
P58ST0.0000.0000.0000.0000.0000.0690.0000.0740.1110.1150.0791.0000.0530.057-0.1720.005-0.0200.126-0.0030.0340.1130.021
P9STGBS0.1830.1860.1250.1330.0730.1460.1360.1280.1110.0990.0000.0531.0000.0760.127-0.0240.0090.0680.0590.0420.0980.106
REEDAD0.1210.1080.1370.1170.1040.1400.1180.0720.0860.1130.1060.0570.0761.000-0.217-0.1460.0620.0860.1130.2820.0690.112
REEEDUC.10.020-0.047-0.050-0.069-0.132-0.184-0.0570.032-0.086-0.089-0.060-0.1720.127-0.2171.000-0.031-0.1610.341-0.196-0.2790.0880.000
S18.A-0.056-0.068-0.095-0.061-0.082-0.012-0.021-0.0080.0260.007-0.0240.005-0.024-0.146-0.0311.0000.0170.2430.0210.0150.0740.361
S20.1140.0990.1000.0600.1240.0540.0930.0950.0290.0350.033-0.0200.0090.062-0.1610.0171.0000.1470.1070.2710.1330.000
S20.J0.1050.0630.0570.0610.1280.1190.1010.0950.0790.0710.1220.1260.0680.0860.3410.2430.1471.0000.2260.2880.0690.044
S30.0330.0690.0820.0680.0890.0260.085-0.0070.0050.0410.057-0.0030.0590.113-0.1960.0210.1070.2261.0000.3490.0550.112
S50.1410.1360.1090.1430.1400.1190.1360.0530.0740.0920.1580.0340.0420.282-0.2790.0150.2710.2880.3491.0000.1210.098
S60.0880.0780.0410.0490.0000.0000.0450.0810.0650.0000.0530.1130.0980.0690.0880.0740.1330.0690.0550.1211.0000.010
SEXO0.0950.1610.0850.0770.0400.1300.1180.0930.0830.1140.1310.0210.1060.1120.0000.3610.0000.0440.1120.0980.0101.000

Missing values

2025-07-30T20:27:58.600402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-30T20:27:59.056422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDENPASEXOP9STGBSP13STGBS.BP13ST.DP13ST.EP13ST.FP13ST.HP40STGBSP41ST.AP41ST.HP44ST.BP45ST.AP45S.BP58STS2S3S5S6S18.AS20.JREEEDUC.1REEDAD
156046042213333232233133347171
1560560412233232333234312-57161
15606604124443-1443433453441244
156076041144442222213443326224
156086041234443322222441242154
156096041234442134211251241173
156106042244444323433433341252
156116041233332234332442441212
156126041133322313422441241223
156136041213331122133331222172
IDENPASEXOP9STGBSP13STGBS.BP13ST.DP13ST.EP13ST.FP13ST.HP40STGBSP41ST.AP41ST.HP44ST.BP45ST.AP45S.BP58STS2S3S5S6S18.AS20.JREEEDUC.1REEDAD
167946041244444432333254441214
167956042223332333232131241172
167966041222332433233241144-251
167976041234333442333433341214
167986042214412412332231243172
167996042234443322333441246172
168006041234433223333442343271
168016041234323333233141143161
1680260422444-13343-122442341153
168036041244444422322431143172